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Introduction: Gliomas are still considered as challenging in oncologic management despite the developments in treatment approaches. The complete elimination of a glioma might not be possible even after a treatment and assessment of therapeutic response is important to determine the future course of actions for patients with such cancers. In the recent years radiomics has emerged as a promising solution with potential applications including prediction of therapeutic response. Hence, this study was focused on investigating whether morphometry-based radiomics signature could be used to predict therapeutic response in patients with gliomas following radiotherapy., Methods: 105 magnetic resonance (MR) images including segmented and non-segmented images were used to extract morphometric features and develop a morphometry-based radiomics signature. After determining the appropriate machine learning algorithm, a prediction model was developed to predict the therapeutic response eliminating the highly correlated features as well as without eliminating the highly correlated features. Then the model performance was evaluated., Results: Tumor grade had the highest contribution to develop the morphometry-based signature. Random forest provided the highest accuracy to train the prediction model derived from the morphometry-based radiomics signature. An accuracy of 86% and area under the curve (AUC) value of 0.91 were achieved for the prediction model evaluated without eliminating the highly correlated features whereas accuracy and AUC value were 84% and 0.92 respectively for the prediction model evaluated after eliminating the highly correlated features., Discussion: Nonetheless, the developed morphometry-based radiomics signature could be utilized as a noninvasive biomarker for therapeutic response in patients with gliomas following radiotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sherminie, Jayatilake, Hewavithana, Weerakoon and Vijithananda.)

The use of fluoroscopy in orthopaedic operations cause a risk for radiation-induced carcinoma for the theatre staff. Thus, the aim of the study was to calculate the occupational radiation doses (ORD) for the orthopaedic theatre crew during fluoroscopy-guided surgeries and to compare them with accepted reference standards. The end Dose Area Products (DAP) were recorded for the commonest 3 types of orthopaedic surgeries by observing 50 surgeries in 2 hospital. The ORD for 4 types of theatre personnel were simulated using. ORDs were statistically analyzed. The calculated mean annual dosage was 0.977mSv. All dosages fell significantly below the reference threshold (20mSv) or less annually. The mean doses that each member of the staff received during the same surgery was different from one another. The orthopaedic surgeon predominantly receives the highest ORD (88.89%), whilst the anaesthetist receives the least (77.78%).

Introduction: Breast density plays a significant role in increasing an individual's risk of breast cancer and its mortality rate., Objectives: We aimed to assess the correlations of mammographic breast density with age, body mass index, weight, height and parity for the first time in Sri Lankan women., Methods: 52 participants who underwent diagnostic mammographic examinations at a tertiary care hospital in Sri Lanka were selected for the study. Demographic data and digital mammograms in DICOM format were collected. Mammographic breast density was quantitatively estimated using a validated, semi-automated computer programme devised by the authors using Java programming language., Results: 65.4% of the participants were postmenopausal, and 34.6% were premenopausal. Mammographic breast density showed a significant negative correlation with age (r = -0.40, p < 0.05) and significant positive correlations with body mass index (r = 0.49, p< 0.05) and weight (r = 0.52, p< 0.05). The study did not find any correlation between mammographic breast density and height. Additionally, it did not find a significant difference between right and left breasts or between parous and nulliparous patients. Mammographic breast density was significantly higher among premenopausal patients compared to postmenopausal patients., Conclusions: Quantitative mammographic breast density demonstrated significant correlations with age, body mass index and weight. The findings of the study will be constructive in predicting breast density in the future and individualizing the breast cancer screening requirements based on the breast density without radiation exposure for females in Sri Lanka.

Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors., Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed., Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process., Conclusions: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies., (© 2022. The Author(s).)